Update README.md

README.md

@@ -13,6 +13,7 @@ tags:
 - '#keras'
 - '#tensorflow'
 - '#diffusers'
+base_model: CompVis/stable-diffusion-v1-4
 ---
 
 # Model Card for Model ID
@@ -34,4 +35,44 @@ This model uses the KerasCV implementation of stability.ai's text-to-image model
 - **Model type:** Diffusion-based text-to-image generative model
 - **Language(s) (NLP):** Python 
 - **License:** CreativeML Open RAIL++-M License
-- **Finetuned from model [https://huggingface.co/CompVis/stable-diffusion-v1-4]:** https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion
+- **Finetuned from model [https://huggingface.co/CompVis/stable-diffusion-v1-4]:** https://github.com/keras-team/keras-cv/tree/master/keras_cv/models/stable_diffusion
+
+
+## To Generate your own Examples:
+Install Dependencies
+!pip install keras-cv==0.6.0 -q
+!pip install -U tensorflow -q
+!pip install keras-core -q
+Imports
+from textwrap import wrap
+import os
+import keras_cv
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import tensorflow.experimental.numpy as tnp
+from keras_cv.models.stable_diffusion.clip_tokenizer import SimpleTokenizer
+from keras_cv.models.stable_diffusion.diffusion_model import DiffusionModel
+from keras_cv.models.stable_diffusion.image_encoder import ImageEncoder
+from keras_cv.models.stable_diffusion.noise_scheduler import NoiseScheduler
+from keras_cv.models.stable_diffusion.text_encoder import TextEncoder
+from tensorflow import keras
+Create a base Stable diffusion Model
+my_base_model = keras_cv.models.StableDiffusion(img_width=512, img_height=512)
+Load Weights from our h5 model which is hosted on Hugging Face here:
+my_base_model.diffusion_model.load_weights('/path/to/file/renaissance_model.h5')
+Create a variable to hold the values of the to-be-generated image such as prompt, batch size, iterations, and seed
+img = my_base_model.text_to_image(
+       prompt="A woman with an enigmatic smile against a dark background",
+       batch_size=1,  # How many images to generate at once
+       num_steps=25,  # Number of iterations (controls image quality)
+       seed=123,  # Set this to always get the same image from the same prompt
+    )
+Display using the function:
+def plot_images(images):
+    plt.figure(figsize=(5, 5))
+    plt.imshow(images)
+    plt.axis("off")
+    
+plot_images(img)